Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "98" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 43 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 41 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459858 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 1.60% | -1.054735 | 0.943391 | 0.091006 | 0.730048 | 0.397493 | -0.156854 | 0.316843 | 1.746047 | 0.6895 | 0.6603 | 0.4221 | 1.487825 | 1.472001 |
| 2459857 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 1.101641 | 1.272374 | -2.593246 | -2.786074 | -0.444561 | -0.100569 | 0.239378 | 0.115206 | 0.0298 | 0.0333 | 0.0032 | nan | nan |
| 2459856 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -1.067584 | 6.114701 | -0.480884 | -0.198762 | -0.154626 | 3.000313 | 0.494991 | 1.973199 | 0.6814 | 0.6756 | 0.4037 | 3.065224 | 2.812554 |
| 2459855 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 4.58% | 0.00% | -1.456268 | 2.755517 | -0.917311 | -0.110672 | 1.049615 | -0.063801 | -0.339150 | 0.724146 | 0.6579 | 0.6951 | 0.4368 | 1.703942 | 1.631269 |
| 2459854 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -1.447157 | 8.515201 | -0.820563 | -0.386061 | 1.179081 | 0.866542 | -0.468554 | 2.127459 | 0.6779 | 0.7098 | 0.4334 | 3.154058 | 2.874582 |
| 2459853 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -1.021342 | 4.070709 | -0.782453 | -0.333311 | 0.054953 | 1.316795 | 1.041460 | 3.113092 | 0.7075 | 0.6667 | 0.4264 | 3.495829 | 3.294839 |
| 2459852 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 8.65% | 8.11% | -0.433671 | 0.279734 | -0.363019 | -0.672892 | -0.306013 | 1.951584 | -0.087140 | 0.318994 | 0.8000 | 0.8130 | 0.2544 | 3.435782 | 3.877200 |
| 2459850 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.395414 | 4.483375 | -0.519542 | -0.359627 | 0.819951 | 2.163871 | 0.429064 | 2.642514 | 0.7103 | 0.7326 | 0.3605 | 2.974614 | 2.773775 |
| 2459849 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.044690 | 7.414675 | -0.511353 | 0.022267 | 1.429797 | 1.868050 | 0.873618 | 2.783644 | 0.7064 | 0.7221 | 0.3696 | 3.695928 | 3.293523 |
| 2459848 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.366925 | 6.167244 | -0.527001 | -0.473440 | 2.378587 | 4.805318 | 0.845698 | 2.061888 | 0.6788 | 0.7255 | 0.3889 | 2.887459 | 2.803626 |
| 2459847 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 3.74% | 1.07% | -0.347889 | 2.917812 | -0.747828 | -0.763131 | 1.352999 | 2.749090 | 0.081743 | 1.262448 | 0.6887 | 0.6604 | 0.4333 | 1.684460 | 1.593088 |
| 2459846 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 33.33% | 0.00% | 0.176759 | 3.067083 | 0.353906 | -0.683303 | -0.553929 | 0.737738 | -0.176088 | 0.977836 | 0.8170 | 0.6512 | 0.5053 | 1.617979 | 1.474150 |
| 2459845 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459844 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459843 | digital_ok | 100.00% | 1.20% | 0.66% | 0.00% | 100.00% | 0.00% | -0.353480 | 15.241641 | -0.323788 | 1.988528 | 4.838051 | 9.178963 | 0.309670 | 2.066807 | 0.7121 | 0.7064 | 0.3836 | 4.170076 | 3.349867 |
| 2459840 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.911876 | 0.823245 | 2.539580 | 3.071302 | 0.071377 | -0.654228 | -1.315640 | -2.050489 | 0.0290 | 0.0271 | 0.0025 | nan | nan |
| 2459839 | digital_ok | 100.00% | - | - | - | - | - | -0.101825 | -0.278516 | 17.766190 | 18.537994 | 1.633444 | 1.323543 | 7.050194 | 4.172109 | nan | nan | nan | nan | nan |
| 2459838 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.670153 | 9.142041 | -0.019805 | 3.611622 | -0.055120 | 8.616785 | -0.222730 | 2.062687 | 0.7152 | 0.6689 | 0.4101 | 4.028109 | 4.025549 |
| 2459836 | digital_ok | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0556 | 0.0811 | 0.0136 | nan | nan |
| 2459835 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -1.023772 | -0.307577 | -0.206926 | -0.654591 | -0.407953 | -1.031147 | 0.964769 | 0.958052 | 0.0585 | 0.0795 | 0.0179 | nan | nan |
| 2459833 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 2.051190 | 1.714467 | 1.782563 | 1.871039 | 0.618159 | -0.236177 | 0.362158 | -0.263942 | 0.0761 | 0.0696 | 0.0109 | nan | nan |
| 2459832 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.288022 | 1.047350 | 0.550868 | 1.343001 | 0.456007 | 0.877177 | 0.128436 | 1.260716 | 0.7972 | 0.5160 | 0.5797 | 1.927829 | 1.779421 |
| 2459831 | digital_ok | 100.00% | 44.89% | 44.89% | 0.00% | - | - | 0.007958 | 1.730085 | 20.955012 | 22.226568 | 1.522794 | 2.014899 | -0.968680 | -1.494683 | 0.2556 | 0.3172 | 0.0342 | nan | nan |
| 2459830 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.920024 | 3.295297 | 1.450828 | 2.206167 | 0.105484 | 0.159201 | -0.104131 | 2.028153 | 0.7976 | 0.5376 | 0.5570 | 1.970823 | 1.539220 |
| 2459829 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.804069 | 53.377554 | 1.432529 | 2.050138 | 9.653216 | 13.924766 | 3.475260 | 12.638944 | 0.7318 | 0.6359 | 0.3926 | 15.203077 | 19.777291 |
| 2459828 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.644231 | 3.620381 | 0.147665 | 1.681561 | -0.202129 | 0.101915 | 7.886388 | 3.234408 | 0.8018 | 0.5406 | 0.5427 | 4.131771 | 4.993211 |
| 2459827 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.382738 | 14.408278 | 2.027328 | 1.416582 | 10.379716 | 12.184975 | 7.004963 | 3.969357 | 0.7333 | 0.6420 | 0.4150 | 12.544955 | 9.961005 |
| 2459826 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.618929 | 4.408798 | 1.381404 | 2.855589 | 0.947464 | 0.998754 | 0.573400 | 2.185840 | 0.7916 | 0.5230 | 0.5406 | 0.000000 | 0.000000 |
| 2459825 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 1.543848 | 1.543209 | 0.597432 | 2.390141 | -0.036245 | -0.503407 | 0.364626 | -0.472318 | 0.7952 | 0.5541 | 0.5258 | 2.630643 | 2.292053 |
| 2459824 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.574170 | 1.307619 | 1.685557 | 1.408176 | 1.477048 | 1.479493 | 0.570196 | 0.820760 | 0.6825 | 0.7093 | 0.3825 | 1.910684 | 2.021508 |
| 2459823 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.721086 | 1.848214 | 1.381465 | 3.193632 | 2.267821 | -0.094527 | 20.047806 | 4.246186 | 0.7410 | 0.6243 | 0.4778 | 66.875538 | 42.995406 |
| 2459822 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.468511 | 5.831967 | 0.783605 | 3.158203 | 0.321509 | -0.364252 | 3.863716 | -0.085432 | 0.7907 | 0.5608 | 0.5257 | 5.435048 | 5.836832 |
| 2459821 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.855770 | 16.308091 | 0.778987 | 2.386459 | -0.849238 | 6.359523 | -0.925050 | 0.110217 | 0.7891 | 0.5766 | 0.5195 | 4.939273 | 5.318684 |
| 2459820 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.776769 | 40.302846 | 1.181207 | 2.920012 | 0.887483 | 24.796942 | 1.500698 | 8.765674 | 0.7450 | 0.6328 | 0.4139 | 4.998102 | 5.104256 |
| 2459817 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 2.087357 | 1.232046 | 0.254780 | 2.729951 | -0.348081 | -0.414831 | -0.354952 | -0.387182 | 0.7940 | 0.6181 | 0.5207 | 2.083211 | 2.160426 |
| 2459816 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.898450 | 0.155929 | 1.488156 | 4.082724 | 2.096638 | 0.546343 | 3.937933 | 3.253416 | 0.8387 | 0.5652 | 0.6085 | 4.001535 | 4.459679 |
| 2459815 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.584473 | 3.400186 | -0.444969 | 3.993078 | 9.096844 | 0.421178 | 393.177359 | 3.162789 | 0.7952 | 0.6408 | 0.5168 | 3.970356 | 3.486497 |
| 2459814 | digital_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.812876 | 10.807541 | 0.258510 | 8.076965 | 1.331599 | 23.931447 | 1.704898 | 2.814562 | 0.7659 | 0.7123 | 0.4169 | 7.029944 | 8.029348 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Temporal Discontinuties | 1.746047 | 0.943391 | -1.054735 | 0.730048 | 0.091006 | -0.156854 | 0.397493 | 1.746047 | 0.316843 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Shape | 1.272374 | 1.272374 | 1.101641 | -2.786074 | -2.593246 | -0.100569 | -0.444561 | 0.115206 | 0.239378 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Shape | 6.114701 | -1.067584 | 6.114701 | -0.480884 | -0.198762 | -0.154626 | 3.000313 | 0.494991 | 1.973199 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Shape | 2.755517 | 2.755517 | -1.456268 | -0.110672 | -0.917311 | -0.063801 | 1.049615 | 0.724146 | -0.339150 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Shape | 8.515201 | 8.515201 | -1.447157 | -0.386061 | -0.820563 | 0.866542 | 1.179081 | 2.127459 | -0.468554 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Shape | 4.070709 | 4.070709 | -1.021342 | -0.333311 | -0.782453 | 1.316795 | 0.054953 | 3.113092 | 1.041460 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Temporal Variability | 1.951584 | -0.433671 | 0.279734 | -0.363019 | -0.672892 | -0.306013 | 1.951584 | -0.087140 | 0.318994 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Shape | 4.483375 | -0.395414 | 4.483375 | -0.519542 | -0.359627 | 0.819951 | 2.163871 | 0.429064 | 2.642514 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Shape | 7.414675 | -0.044690 | 7.414675 | -0.511353 | 0.022267 | 1.429797 | 1.868050 | 0.873618 | 2.783644 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Shape | 6.167244 | 6.167244 | -0.366925 | -0.473440 | -0.527001 | 4.805318 | 2.378587 | 2.061888 | 0.845698 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Shape | 2.917812 | 2.917812 | -0.347889 | -0.763131 | -0.747828 | 2.749090 | 1.352999 | 1.262448 | 0.081743 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Shape | 3.067083 | 0.176759 | 3.067083 | 0.353906 | -0.683303 | -0.553929 | 0.737738 | -0.176088 | 0.977836 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Shape | 15.241641 | 15.241641 | -0.353480 | 1.988528 | -0.323788 | 9.178963 | 4.838051 | 2.066807 | 0.309670 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Power | 3.071302 | 0.911876 | 0.823245 | 2.539580 | 3.071302 | 0.071377 | -0.654228 | -1.315640 | -2.050489 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Power | 18.537994 | -0.278516 | -0.101825 | 18.537994 | 17.766190 | 1.323543 | 1.633444 | 4.172109 | 7.050194 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Shape | 9.142041 | 9.142041 | -0.670153 | 3.611622 | -0.019805 | 8.616785 | -0.055120 | 2.062687 | -0.222730 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | ee Temporal Discontinuties | 0.964769 | -0.307577 | -1.023772 | -0.654591 | -0.206926 | -1.031147 | -0.407953 | 0.958052 | 0.964769 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | ee Shape | 2.051190 | 1.714467 | 2.051190 | 1.871039 | 1.782563 | -0.236177 | 0.618159 | -0.263942 | 0.362158 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Power | 1.343001 | 0.288022 | 1.047350 | 0.550868 | 1.343001 | 0.456007 | 0.877177 | 0.128436 | 1.260716 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Power | 22.226568 | 0.007958 | 1.730085 | 20.955012 | 22.226568 | 1.522794 | 2.014899 | -0.968680 | -1.494683 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Shape | 3.295297 | 0.920024 | 3.295297 | 1.450828 | 2.206167 | 0.105484 | 0.159201 | -0.104131 | 2.028153 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Shape | 53.377554 | 53.377554 | 0.804069 | 2.050138 | 1.432529 | 13.924766 | 9.653216 | 12.638944 | 3.475260 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | ee Temporal Discontinuties | 7.886388 | 3.620381 | 1.644231 | 1.681561 | 0.147665 | 0.101915 | -0.202129 | 3.234408 | 7.886388 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Shape | 14.408278 | 1.382738 | 14.408278 | 2.027328 | 1.416582 | 10.379716 | 12.184975 | 7.004963 | 3.969357 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Shape | 4.408798 | 4.408798 | 1.618929 | 2.855589 | 1.381404 | 0.998754 | 0.947464 | 2.185840 | 0.573400 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Power | 2.390141 | 1.543209 | 1.543848 | 2.390141 | 0.597432 | -0.503407 | -0.036245 | -0.472318 | 0.364626 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | ee Power | 1.685557 | 0.574170 | 1.307619 | 1.685557 | 1.408176 | 1.477048 | 1.479493 | 0.570196 | 0.820760 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | ee Temporal Discontinuties | 20.047806 | 1.848214 | 1.721086 | 3.193632 | 1.381465 | -0.094527 | 2.267821 | 4.246186 | 20.047806 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Shape | 5.831967 | 1.468511 | 5.831967 | 0.783605 | 3.158203 | 0.321509 | -0.364252 | 3.863716 | -0.085432 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Shape | 16.308091 | 16.308091 | 2.855770 | 2.386459 | 0.778987 | 6.359523 | -0.849238 | 0.110217 | -0.925050 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Shape | 40.302846 | 1.776769 | 40.302846 | 1.181207 | 2.920012 | 0.887483 | 24.796942 | 1.500698 | 8.765674 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Power | 2.729951 | 2.087357 | 1.232046 | 0.254780 | 2.729951 | -0.348081 | -0.414831 | -0.354952 | -0.387182 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Power | 4.082724 | 0.155929 | 1.898450 | 4.082724 | 1.488156 | 0.546343 | 2.096638 | 3.253416 | 3.937933 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | ee Temporal Discontinuties | 393.177359 | 3.400186 | 1.584473 | 3.993078 | -0.444969 | 0.421178 | 9.096844 | 3.162789 | 393.177359 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 98 | N07 | digital_ok | nn Temporal Variability | 23.931447 | 10.807541 | 2.812876 | 8.076965 | 0.258510 | 23.931447 | 1.331599 | 2.814562 | 1.704898 |